Retail Deployment Model

ABSTRACT

A method of determining and optimizing the location of a new insurance agency is disclosed to increase market penetration of underrepresented markets. The method comprises the use of a scoring algorithm to rank various geographic regions or related zip codes. The scoring algorithm may be implemented by a location modeling system based on variables selected by a user.

This application claims the benefit of U.S. Provisional Application No.60/743,295, filed Feb. 15, 2006, which is incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

The present invention relates to business location modeling systems andmethods. More particularly, the invention relates to insurance agencylocation modeling to establish new insurance agency locations in variousgeographic locations based on an evaluation of user selected criteria.

DESCRIPTION OF THE RELATED ART

The decision to open a new office or branch in order to increase salesfor various different types of products or services in a particulargeographical region or postal zip code can involve consideration ofnumerous factors such as population density, potential populationgrowth, customer household data, and traffic flow patterns. Whendetermining a location for a new office or branch for an insurancecompany, a business entity or business owner may desire to consistentlyuse the same factors or analysis when comparing different geographicallocations for the new office site. The use of inconsistent data orfactors across geographic locations may result in a suboptimal sitelocation being selected for a new office or branch.

Moreover, as the number of potential geographic regions increases, itcan be desirable to normalize results so that comparisons between thedifferent geographic regions may be utilized. For example, a companyproviding a particular product or service may wish to open a number ofnew offices or branches across a large geographic region such as theUnited States. With such a large geographic region to consider, it canbe desirable to display comparable results to decision makers so thatsuitable site locations may be selected.

Current site location models in use in other industries such as retailpharmacy do not take into account unique factors and problems found inthe insurance industry. In addition, existing site location models orsystems of other industries may not utilize an overall scoring methodthat allows results to be consistently and easily displayed to thedecision maker or business entity. Without an overall scoringmethodology, the analysis of the results is more time consuming andinefficient involving the unnecessary consumption of numerous resources.

Therefore, there is a need in the art for an insurance agency locationmodeling method and system regarding the process of determiningsuccessful placement of future insurance agency locations. The methodand system for suitable insurance agency locations must provideconsistent and easily interpreted results.

SUMMARY

Aspects of the present invention overcome problems and limitations ofthe prior art by providing a method of determining suitable locationsfor new insurance agency locations. The disclosed method may be utilizedto increase market penetration of underrepresented markets. The methodcomprises the use of a scoring algorithm to rank various geographicalregions by related zip codes. The scoring algorithm may be implementedby a location modeling system based upon markets selected by a user.

In an exemplary aspect of the invention, a user may select ageographical region to be evaluated for placement of an insurance agencylocation. The geographical region may be in the form of a postal zipcode. Various modeling factors are used to determine a highly suitablelocation for the new insurance agency. A score for each zip code iscalculated with the highest overall score representing the most highlysuitable, preferred or optimized location for the new office or branch.

In certain embodiments of the invention, the present invention can bepartially or wholly implemented with a computer-readable medium, forexample, by storing computer-executable instructions or modules, or byutilizing computer-readable data structures. Of course, the methods andsystems of the above-referenced embodiments may also include otheradditional elements, steps, computer-executable instructions, orcomputer-readable data structures.

The details of these and other embodiments of the present invention areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may take physical form in certain parts and steps,embodiments of which will be described in detail in the followingdescription and illustrated in the accompanying drawings that form apart hereof, wherein:

FIG. 1 shows a diagram of a computer system that may be used toimplement aspects of the invention.

FIG. 2 illustrates a method of application of the insurance agencylocation modeling system, in accordance with an embodiment of theinvention.

FIG. 3 illustrates an exemplary user interface screen for a section ofthe agency deployment modeling system, in accordance with an aspect ofthe invention.

FIG. 4 illustrates an exemplary user interface screen enabling a user toselect various geographic regions, in accordance with an aspect of theinvention.

FIG. 5 illustrates the selection of a predefined template, in accordancewith an aspect of the invention.

FIGS. 6 a, 6 b, and 7 illustrate the calculation of a final score forselected zip codes, in accordance with an aspect of the invention.

FIG. 8 illustrates the display of the final score along with additionalprofile information associated with the particular zip code, inaccordance with an aspect of the invention.

FIG. 9 illustrates a map in which a particular zip code may be viewed bya user, in accordance with an aspect of the invention

DETAILED DESCRIPTION Exemplary Operating Environment

FIG. 1 shows a diagram of a computer system that may be used toimplement aspects of the invention. A plurality of computers, such asresearch workstations 102 and 104, may be coupled to a user computer 112via networks 108 and 118. User computer 112 may be coupled to a datasolutions transaction manager computer 110, which is described in detailbelow. User computer 112 provides decision makers with a user interfaceon user workstations 112, 114 and 116 for displaying policy informationand decisions such as potential new agency locations, and enables usersto interact with data solutions transaction manager computer 110.

User workstations 112, 114 and 116 and research workstations 102 and 104may require information from external data sources to assist evaluationof a potential new agency location. Requests for such information may betransmitted via data solutions transaction manager computer 110 to adata gathering system 120. Data gathering system 120 may include aprocessor, memory and other conventional computer components and may beprogrammed with computer-executable instructions to communicate withother computer devices. Data gathering system 120 may access externalsources of information, such as information vendors 122, 124 and 126 viathe Internet 128. Information vendors may include federal or stateagencies that provide aggregate motor vehicle data, census information,or vendors that provide demographic information, maps and geographicallocation information, and other information that may be used to evaluatepotential agency locations.

Data solutions transaction manager 110 may be programmed withcomputer-executable instructions to receive requests for data from usercomputers 112, 114 and 116 and research workstations 102 and 104, formatthe requests and transmit the requests to data gathering system 120. Inone embodiment of the invention, requests for data are in the form ofdocuments that are in extensible markup language (XML) format. Datasolutions transaction manager 110 may also be coupled to a data managercomputer device 130 that accesses customer data stored in a datarepository 132. In one embodiment of the invention, all data gathered ona customer or potential customer is stored in data repository 132 sothat when additional requests are made for the same data, the data mayquickly be obtained without requesting it from information vendors 122,124 and 126. Data repository 132 may be implemented with a group ofnetworked server computers or other storage devices.

Decision makers may be provided with a user interface on user computers112, 114 and 116 for displaying policy information and decisions, andenables users to interact with data solutions transaction manager 110.The user interface may allow a user or decision maker to perform avariety of functions, such as entering local market data into analysisreport templates, and displaying decision results. In addition, usersmay execute various analysis tools to answer questions such as: 1)“Where is the best location for a new office?”, 2) “What is the makeupof the population?”, 3) “Where are our competitors' offices?”, 4) “Whichmarkets will experience household and vehicle growth?”, and 5) “Arethere enough prospects that own homes in a three mile radius around aparticular agent's office?”

In an embodiment, senior decision makers may use the system to improvetheir understanding of the marketplace, facilitating business decisions.The user may select various geographic regions and run various reportsto obtain agency deployment information.

One or more of the computer devices and terminals shown in FIG. 1 mayinclude a variety of interface units and drives for reading and writingdata or files. One skilled in the art will appreciate that networks 108,118 and 128 are for illustration purposes and may be replaced with feweror additional computer networks. One or more networks may be in the formof a local area network (LAN) that has one or more of the well-known LANtopologies and may use a variety of different protocols, such asEthernet. One or more of the networks may be in the form of a wide areanetwork (WAN), such as the Internet. Computer devices and other devicesmay be connected to one or more of the networks via twisted pair wires,coaxial cable, fiber optics, radio waves or other media.

The term “network” as used herein and depicted in the drawings should bebroadly interpreted to include not only systems in which remote storagedevices are coupled together via one or more communication paths, butalso stand-alone devices that may be coupled, from time to time, to suchsystems that have storage capability. Consequently, the term “network”includes not only a “physical network” but also a “content network,”which is comprised of the data—attributable to a single entity—whichresides across all physical networks.

Exemplary Embodiments

FIG. 2 illustrates a method of determining a suitable location of aninsurance agency location in an embodiment of the invention. The methodmay be implemented by a location modeling system being executed on acomputer such as data solutions transaction manager 110. The method ofFIG. 2 will be illustrated in the following exemplary embodiment. FIG. 3illustrates a user interface screen 302 that may be presented to a userby the data solutions transaction manger 110. The user interface screen302 may enable a user to execute a number of different analysis toolssuch as agency deployment tool 304, a report generation tool 306, alocal market analysis tool 308, atlas tools 310 and 312, and/or alibrary of generated reports tool 314. A user may execute the agencydeployment tool 304 by clicking on the agency deployment box.

Upon activation of the agency deployment tool 304, in a first step 202,a user selects at least one geographic region to be evaluated forplacement of a new insurance agency location. The geographic region maybe a region of the United States such as the Midwest or may be acombination of various different states, cities, towns, neighborhoods,or other geographic identifiable regions. Those skilled in the art willrealize that numerous different geographic regions and combinations maybe defined for analysis. For instance, FIG. 4 illustrates a userinterface screen 402 that enables a user to select various U.S. Statesfor analysis based on selection box 403.

A user may create a customized geographic region for ease of use infuture sessions. For example, a user may select California, Florida,Illinois, New York, Ohio, and Texas using the add button 404 and definethese selected States (405) as the “Large States” 406. Similarly, a usermay edit their customized selection through the use of the remove button407. A user may save their customized selection using the “Save Changes”button 408. Once saved, a user may select their customized selectionduring future modeling session as illustrated in FIG. 5 in the “ChooseYour Geography” selection screen 502.

Based on the user selected geographic regions, the underlying or relatedzip codes for selected geographic regions are determined in step 204.The use of zip codes enables all of the collected data from various datasources to be converted into data that may be used and scored on acommon metric scale. The common metric scale allows various informationsources to be integrated and scored. Those skilled in the art willrealize that distinguishable data other than zip codes may be used inorder to allow processing of data on a common metric scale.

Next, in step 206, data is received from the user which includes atleast one modeling factor to be utilized in the determination of theinsurance agency location. Those skilled in the art will realize thatany number or combination of modeling factors may be used depending uponthe marketing or agency growth strategy.

The modeling factors may include one of the following exemplaryfactors: 1) households with 2+ vehicles current year estimate; 2)households with 2+ vehicles five year projection; 3) net change vehiclehouseholds in five years; 4) owner occupied dwellings current yearestimate; 5) owner occupied dwellings five year projection; 6) netchange in owner occupied dwellings in five years; 7) total householdscurrent year estimate; 8) total household five year project; 9) netchange (number and %) in households in five years; 10) new movers; 11)new homeowners; 12) average household net worth; 13) average householdincome; 14) population 25+ years old; 15) population 25+ with somecollege education; 16) population 25+ with associate degree; 17)population 25+ with bachelor's degree; 18) population 25+ with graduateor professional degree; 19) percent population 25+ any collegeeducation; 20) households with length of residence less than one year;21) percent households with length of residence less than one year; 22)active property insurance casualty policies; 23) active life/financialinsurance policies; 24) active insurance policies; 25) total insurancecustomer households; 26) total insurance customer household lifetimevalue; 27) average insurance customer household lifetime value; 28)value of expansion opportunity; and 29) new businesses.

As those skilled in the art will realize, the above modeling factorsand/or combinations of modeling factors do not represent an exhaustivelist of modeling factors that may used in the determination of agencylocations. As an alternative, to the individual selection of each of themodeling factors, templates may be defined with particular modelingfactors to be used in the agency location model. For example, templatessuch as “Established but Still Growing” template 504 in FIG. 5, and a“Communities in Progress” template 506 may be selected from a group ofpreexisting templates. Each template may contain modeling factors thathave been proven to be statistically important when trying to identifyparticular growth opportunities such as finding markets that haveexisting neighborhoods that are still growing. The following factors maybe preselected to be used with the “Established but Still Growing”template 504: 1) net change (number and %) in households in five years;2) percent population 25+ years old with any college education; 3)percent households with length of residence less than one year; 4) totalinsurance customer households; and 5) average insurance customerhousehold lifetime value. Average insurance customer household lifetimevalue may be calculated by subtracting projected expenses for a customerfrom projected revenue for each calendar year over a time span of aprojected retention period. These values may then be averaged to computethe average insurance customer lifetime value. These are calculated andaccessible for use in the model via research network 108.

In another example, the “Communities in Progress” template 506 mayidentify growth opportunities in generally smaller communitiesexperiencing recent change. Modeling factors that may be utilized whenthe “Communities in Progress” template 506 is used may include: 1) newmovers; 2) new homeowners; 3) percent households with length ofresidence less than one year; and 4) new businesses.

In step 208, a final score per zip code may be calculated. Thecalculation of the scores may be determined by the following equations:

$\begin{matrix}{\frac{{{Variable}\mspace{11mu} 1\mspace{14mu} {information}} - {{Variable}\mspace{11mu} 1\mspace{11mu} \mu}}{{Variable}\mspace{14mu} 1\mspace{11mu} \sigma} = {{Variable}\mspace{11mu} 1\mspace{14mu} {score}}} & {{Equation}\mspace{14mu} 1} \\{{\left( {{Variable}\mspace{14mu} 1\mspace{14mu} {score}*{Variable}\mspace{14mu} 1\mspace{14mu} {weight}} \right) + \left( {{Variable}\mspace{14mu} 2\mspace{14mu} {score}*{Variable}\mspace{14mu} 2\mspace{14mu} {weight}} \right) + \left( {{Variable}\mspace{14mu} 3\mspace{14mu} {score}*{Variable}\mspace{14mu} 3\mspace{14mu} {weight}} \right) + {{other}\mspace{14mu} {Variables}}} = {{Composite}\mspace{14mu} {score}}} & {{Equation}\mspace{14mu} 2} \\{\frac{{{Composite}\mspace{14mu} {Score}} - \mu}{\sigma} = {{Final}\mspace{14mu} {Score}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In equations 1 and 3, μ represents the mean and σ represents standarddeviation of the individual variables and variable scores multiplied bytheir variable weights. A positive score reflects greater potential forfuture business growth in the particular geographic region of interest.A negative score reflects less opportunity for future growth relative tothe variation within each geographic region. For example, FIGS. 6 a, 6b, and 7 illustrate the calculation of a final score for zip codes in astate, “zips 001 to 003” 602, “zips007, 009, 011, 013, and 016” 604, and“zip050” 606. Each of the zip codes represents a geographic regionselected by the user for evaluation. In FIG. 6 a, six variables aremodeled to identify growth opportunities for placement of insuranceagency locations. Variable information 609 for each of the zip codeslisted 602, 604, and 606 is shown in Table 1 of FIG. 6 a. The variableinformation includes counts, dollars, and percentage information for thesix modeling factors.

A score per variable 617 using equation 1 is calculated for each of thezip codes listed 602, 604, and 606 as shown in Table 2 of FIG. 6 a.Table 3 illustrates that a range of weighting factors 621 may be appliedto each of the variables. Those skilled in the art will realize thatdifferent ranges of weighting factors may be used for each of themodeling factors and that the weighting factors may change over time andwith use of model. The weighting factors may be adjusted so that aparticular modeling factor is given more significance in the calculationof the final score. In the examples of FIG. 6 b, each variable can beassigned equal or varying weights. Table 4, using equation 2,illustrates zip codes 602, 604, and 606 with equal weights. Thecalculated composite score for “zip 001” 602 is 0.802451 (616), “zip007”604 is 0.297420 (618), and “zip050” 606 is 0.677538 (620). Table 5illustrates these same zip codes 602, 604, and 606 with varying weights.Table 5 lists the calculated composite score for “zip 001” 602 is0.90198781 (680), “zip 007” 604 is 0.34198808 (682), and “zip 050” 606is 0.87358715 (684).

Next, equation 3 is applied to each of zip codes 602, 604, and 606 asillustrated in Tables 1 and 2 of FIG. 7. For example Table 1 of FIG. 7,calculates the Final Score using equal weights for “zip001” 602 is1.728691 (690), “zip007” 604 is 0.64072 (692), and “zip050” 606 is1.459595 (693). Table 2 illustrates the calculated Final Score usingvarying weights for zips 602, 604, 606. FIG. 7 zip code examples arebased upon actual information for the state of Delaware. The positivescores reflect greater potential for future business growth in theparticular geographic region of interest; whereas, the negative scoresreflect less opportunity for future growth relative to the variationwithin each geographic region. Finally, the zip codes are ranked andcompared in step 210 according to their value 702, 706, and 704 (usesequal weights); 750, 770, and 760 (uses varying weights).

The final scores for each zip code may be displayed along withadditional profile information which may be of interest to the user. Forexample, the final scores may be integrated with profile information tocreate new perspectives and insights regarding each market. An exampleof such profile information is illustrated in FIG. 8. In FIG. 8, a zipcode “802” along with its associated Final Score of 6.93 (804) is shownwith profile information such as number of households in 2003 (806) andpercentage of college education (808). Other profile information thatmay be displayed includes recent change information, projected growthnumber, percent current insurance penetration, number of prospects,current number and type of insurance agencies, and number of competitorinsurance agencies. Profile information is periodically updated(annually, bi-annually, quarterly, or biweekly).

Any of the selected zip codes may be displayed on a map such thatspecific point locations and surrounding areas may be interactivelydefined with respective model outputs and information generated forsurrounding areas. For example, FIG. 9 illustrates a map in which aparticular zip code is illustrated by the region defined at 902. Inaddition, user defined map features may display information such asmodel outputs, competitor agency locations, and other usefulinformation. The maps may also contain three-dimensional aerial imageryand other geographic features (cartography) which may be displayed tothe user. The integration of the results of the final scores along withadditional information such as competitor agency locations andcartography may enable a user to pinpoint a highly suitable potentiallocation for the new agency in a particular neighborhood at a particularstreet address. In addition, reports may be generated detailing thescores for each of the selected zip codes or geographic regions alongwith detailed maps of each of these potential new agency locations.

A few examples of a few embodiments of the invention are provided below.These examples describe only versions of a few embodiments of theinvention. The invention is not limited to the examples described belowand includes numerous additional embodiments and versions. The examplesshould not be read to limit the disclosure of the invention in thisapplication.

EXAMPLE 1

An insurance location is determined through steps of (1) receiving froma user at least one geographic region to be evaluated for placement ofthe insurance agency location, (2) based on the at least one geographicregion received in step (1), determining related zip codes to beevaluated, (3) receiving from the user at least one modeling factor tobe utilized in the determination of the insurance agency location, (4)calculating at a processor a final score for each of the zip codesdetermined in step (2); and (5) comparing the final scores for each ofthe zip codes to determine the zip code with the highest final score Thereceiving, determining, calculating and comparing can be performed by acomputer. They also can be performed by a person. In addition, themodeling factors that can be used include one or more of the following:total and net change in households in current year and last five years;number of new homeowners and new movers; percentage of population withat least twenty five years of age and some college education; percentageof households with length of residency of less than one year; totalnumber of agency customer households; total number of new businesses;agency customer household lifetime value; whether or not householdmaintains 2 or more vehicles; whether a dwelling is owner occupied;average household income and average household net worth.

EXAMPLE 2

The location for an insurance agency is determined using at least thefollowing steps. A geographic region to be evaluated for placement ofthe insurance agency location is received from a user. Based on thisgeographic region, particular zip codes are identified to be evaluated.A composite score is calculated for each zip code. A final score is thencalculated at a processor for each of the zip codes using the formula

$\frac{{{Composite}\mspace{14mu} {Score}} - \mu}{\sigma} = {{Final}\mspace{14mu} {{Score}.}}$

The final scores are compared to each other to determine the relativeranking. The final scores may be displayed on a map and/or summarizedand detailed in a report.

EXAMPLE 3

A computer-readable medium contains computer-executable instructions forcausing a computer device to perform a number of steps. These stepsinclude (a) receiving from a user zip codes to be evaluated forplacement of an insurance agency location; (b) receiving from researchterminal 102, 104 modeling factors to be utilized in the determinationof the insurance agency location; (c) calculating at a processor a finalscore for each of the zip codes received in step (a); (d) comparing thefinal scores for each of the zip codes to determine the zip code withthe highest final score; and (e) displaying the final scores for each ofthe zip codes on a map and/or report that contains at least street levelinformation.

While the invention has been described with respect to specific examplesincluding presently preferred modes of carrying out the invention, thoseskilled in the art will appreciate that there are numerous variationsand permutations of the above described systems and techniques that fallwithin the spirit and scope of the invention.

1. A method of determining an insurance agency location, the methodcomprising: (a) receiving at least two zip codes to be evaluated forplacement of the insurance agency location; (b) receiving at least onemodeling factor to be utilized in the determination of the insuranceagency location; (c) calculating a final score for each of the at leasttwo zip codes; and (d) comparing the final scores for each of the atleast two zip codes.
 2. The method of claim 1, further including: (e)displaying the final scores for each of the at least two zip codes on amap.
 3. The method of claim 2, wherein the map includes street levelinformation.
 4. The method of claim 1, wherein step (c) furthercomprises the steps of: (i) calculating a composite score for each ofthe at least two zip codes; and (ii) calculating the final score foreach of the at least two zip codes based on the equation$\frac{{{Composite}\mspace{14mu} {Score}} - \mu}{\sigma} = {{Final}\mspace{14mu} {{Score}.}}$5. The method of claim 4, wherein the composite score comprises aweighted composite score.
 6. The method of claim 1, wherein the at leastone modeling factor includes total and net change in households incurrent year and last five years.
 7. The method of claim 1, wherein theat least one modeling factor includes number of new homeowners and newmovers.
 8. The method of claim 1, wherein the at least one modelingfactor includes percentage of population age at least twenty-five yearsand having some college education.
 9. The method of claim 1, wherein theat least one modeling factor includes percentage of households withlength of residency of less than one year.
 10. The method of claim 1,wherein the at least one modeling factor includes total number of agencycustomer households.
 11. The method of claim 1, wherein the at least onemodeling factor includes total number of new businesses.
 12. The methodof claim 1, wherein the at least one modeling factor includes agencycustomer household life time value.
 13. The method of claim 1, whereinthe at least one modeling factor includes households with two or morevehicles.
 14. The method of claim 1, wherein the at least one modelingfactor includes owner occupied dwelling.
 15. The method of claim 1,wherein the at least one modeling factor comprises average householdincome and average household net worth.
 16. A method of determining aninsurance agency location, the method comprising: (a) receiving from auser at least one geographic region to be evaluated for placement of theinsurance agency location; (b) based on the received at least onegeographic region received in step (a), determining related zip codes tobe evaluated; (c) receiving from the user modeling factors to beutilized in the determination of the insurance agency location; (d)calculating at a processor a final score for each of the zip codesdetermined in step (b); and (e) comparing the final scores for each ofthe zip codes to determine the zip code with the highest final score.17. The method of claim 16, further including: (f) displaying the finalscores for each of the zip codes on a map.
 18. A method of determiningan insurance agency location, the method comprising: (a) receiving ageographical region to be evaluated for placement of the insuranceagency location; (b) determining zip codes associated with the receivedgeographical region; (c) gathering data associated with the determinedzip codes; (d) selecting a template having various modeling factors tobe utilized in the determination of the insurance agency location; (e)calculating a final score for each of the zip codes determined in step(b); and (f) comparing the final scores for each of the zip codes. 19.The method of claim 18, wherein step (e) further comprises the steps of:(i) calculating a variable score; (ii) calculating a composite score;and (ii) calculating the final score for each zip code based on theequation$\frac{{{Composite}\mspace{14mu} {Score}} - \mu}{\sigma} = {{Final}\mspace{14mu} {{Score}.}}$20. The method of claim 19, wherein the composite score comprises aweighted composite score.
 21. The method of claim 18, wherein themodeling factors comprise net change in number and percentage ofhouseholds in the next five years, percentage of population overtwenty-five years of age with some college education, percentage ofhouseholds with length of residency less than one year, total companyinsurance customer households, and average insurance customer householdlife time value.
 22. The method of claim 18, wherein the modelingfactors comprise new movers, new homeowners, percentage households withlength of residency less than one year or recent change in number ofhouseholds, and new businesses.
 23. The method of claim 18, furtherincluding displaying the calculated final scores on a display.
 24. Themethod of claim 23, wherein the display also includes additionalmodeling factors, profile information, existing agency locations, andcompetitor locations.
 25. The method of claim 23, wherein the displayalso includes a map having aerial imagery.
 26. The method of claim 25,wherein the map is thematic map.
 27. The method of claim 23, wherein thedisplay includes the use of 3D cartography.